Wu-Hua Chen

Brief paper: Delay-dependent robust stabilization for uncertain neutral systems with distributed delays

The problems of robust stability and robust stabilization of uncertain neutral systems with distributed delays are studied in this paper. Using a combination of integral inequality technique and descriptor system approach, new delay-dependent sufficient conditions for robust stability and robust stabilization are formulated in terms of linear matrix inequalities (LMIs). LMI-based conditions are also derived for robust stability and robust stabilizability of uncertain distributed-delay systems when the distributed delay belongs to a given interval. When the results obtained in this paper are applied to stabilization of combustion in the chamber of a liquid monopropellant rocket motor, it is found that the combustion can be robustly stabilized over larger variation intervals of pressure parameter and time-delay parameter than those obtained in Zheng and Frank [(2002). Robust control of uncertain distributed delay systems with application to the stabilization of combustion in rocket motor chambers. Automatica, 38, 487-497].

Delay-dependent exponential stability of uncertain stochastic systems with multiple delays: an LMI approach

In this paper, the problem of exponential stability in mean square for stochastic systems with multiple delays is investigated. A delay-dependent sufficient condition is derived in terms of linear matrix inequalities (LMIs) by using a descriptor model transformation of the system and by applying Moons inequality for bounding cross terms. The criteria obtained in this paper can be tested numerically very efficiently using interior point algorithms. An example shows that the proposed methods are less conservative than the other methods.

An LMI Approach to Exponential Stability Analysis of Neural Networks with Time-Varying Delay

This paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results.

Guaranteed cost control for uncertain Markovian jump systems with mode-dependent time-delays

This note concerns the robust guaranteed cost control problem for a class of continuous-time Markovian jump linear system with norm-bounded uncertainties and mode-dependent time-delays. The problem is to design a memoryless state feedback control law such that the closed-loop system is stochastically stable and the closed-loop cost function value is not more than a specified upper bound for all admissible uncertainties. Based on linear matrix inequality, delay-dependent sufficient conditions for the existence of such controller are derived by using a descriptor model transformation of the system and by applying Moons inequality for bounding cross terms. Sufficient conditions which depend on the difference between the largest and the smallest time-delays are also presented. Two numerical examples are given for illustration of the proposed theoretical results.

Technical Communique: Delay-dependent output feedback guaranteed cost control for uncertain time-delay systems

This paper considers the problem of output-feedback guaranteed cost controller design for uncertain time-delay systems. By representing the time-delay system in the descriptor form and using a recent result on bounding of cross products of vectors, we obtain new delay-dependent sufficient conditions for the existence of the guaranteed cost output-feedback controller in terms of matrix inequalities. A numerical algorithm is proposed to construct a full order output feedback controller achieving a suboptimal guaranteed cost such that the system can be stabilized for all admissible uncertainties. An example is presented which shows that the proposed method can produce a lower guaranteed cost than the delay-independent method.

Brief paper: Input-to-state stability and integral input-to-state stability of nonlinear impulsive systems with delays

This paper is concerned with analyzing input-to-state stability (ISS) and integral-ISS (iISS) for nonlinear impulsive systems with delays. Razumikhin-type theorems are established which guarantee ISS/iISS for delayed impulsive systems with external input affecting both the continuous dynamics and the discrete dynamics. It is shown that when the delayed continuous dynamics are ISS/iISS but the discrete dynamics governing the impulses are not, the ISS/iISS property of the impulsive system can be retained if the length of the impulsive interval is large enough. Conversely, when the delayed continuous dynamics are not ISS/iISS but the discrete dynamics governing the impulses are, the impulsive system can achieve ISS/iISS if the sum of the length of the impulsive interval and the time delay is small enough. In particular, when one of the delayed continuous dynamics and the discrete dynamics are ISS/iISS and the others are stable for the zero input, the impulsive system can keep ISS/iISS no matter how often the impulses occur. Our proposed results are evaluated using two illustrative examples to show their effectiveness.

Delay-Dependent Exponential Stability of Neural Networks With Variable Delay: An LMI Approach

This brief focuses on the problem of delay-dependent stability analysis of neural networks with variable delay. Two types of variable delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing a new type of Lyapunov-Krasovskii functional, new delay-dependent sufficient conditions for exponential stability of delayed neural networks are derived in terms of linear matrix inequalities. We also obtain delay-independent stability criteria. Two examples are presented which show our results are less conservative than the existing stability criteria

Global Exponential Stability of Impulsive Neural Networks With Variable Delay: An LMI Approach

This paper focuses on the problem of global exponential stability analysis of impulsive neural networks with variable delay. Three types of impulses are considered: the impulses are input disturbances; the impulses are ldquoneutralrdquo type (that is, they are neither helpful for stability of neural networks nor destabilizing); and the impulses are stabilizing. For each type of impulses, by using Lyapunov function and Razumikhin-type techniques, the sufficient conditions for global exponential stability are developed in terms of linear matrix inequalities with respect to suitable classes of impulse time sequences. The new sufficient stability conditions do not impose any restriction on the size of time-delay. Numerical examples are given which show our results are less conservative than the existing sufficient stability conditions.

Improved Delay-Dependent Asymptotic Stability Criteria for Delayed Neural Networks

This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov–Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz–Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria.

Impulsive Stabilization and Impulsive Synchronization of Discrete-Time Delayed Neural Networks

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.